Under one current paradigm, advertisements for products and services may be dynamically inserted into designated spaces on web pages creating advertisements known as “banner ads”. A user interested in a product or service featured in a banner ad may click on the ad to reach a website associated with the advertiser. This system does not efficiently link buyers and sellers. For example, a potential buyer may not be interested in an advertised product or service, or the ads do not lead the potential buyer to a range of competitive products and services to evaluate.
A more direct way for potential buyers to find information about a product or service in which they may have current interest is to input words that relate to the product or service into a search engine that searches web sources for information about the product or service. If structured properly, such a search will typically locate information about a range of competitive products. The disadvantage of this system is that along with relevant hits, the search engine will find many hits that are not relevant to the user's interests. Relevant hits may be buried deeply in the list of returned results, and they may be extremely difficult to find among the irrelevant results.
To address this situation, some search portals now provide for selectivity in terms of how results are returned to a user in response to a user's input of search terms (hereinafter search terms in the nature of one or more “keywords”). This creates an opportunity for keywords input into the search engine to determine the ranking of results returned to a user. The assumption is that when such words are entered into a search engine, the user is engaging in a search for related products or services. In a competitive market place, merchants with significant offerings of products and services will want to make such offerings available to potential buyers to communicate price and value opportunities. Accordingly, there is a growing number of search portals that auction search result rankings for predetermined keywords related to a product or service.
An exemplary keyword auction system is seen in Appendix A, which is incorporated by reference. Appendix A is a copy of web pages illustrating and describing features of an online search system of a web search portal operated by Overture (formerly GoTo.com). The first two pages of Appendix A reference a biddable item in the nature of the predetermined keywords “digital camera”. When the keyword is entered into the search engine user interface and a search run, a list of results are returned, as shown. The list is a compilation of links to websites related to the keywords, and the links may include descriptive information about the information on the linked websites. The links are presented in the order of the website that has committed to the highest bid for the keyword. For example, www.xistral.com is at the top of the list. Its bid for the keyword is displayed. It is $1.00. At the bottom of the page, in the number five position, is a link ad.farm.mediaplex.com. It has bid $0.42 for its fifth place ranking. Any number of rankings may be produced in the search results. When a web user clicks on a link, the website is required to pay the auction site the indicated bid amount. A website may competitively change its ranking by changing its bid amount relative to the other current bids. In some cases the bidder is an infomediary that provides services to facilitate transactions between consumers and merchants. For example, the infomediary may bid on a keyword to achieve a desired ranking for the keyword. If the web user clicks on the infomediary's ranked listing in the search results, the user will be taken to the infomediary's website where there may be an aggregation of focused information about the product or service of interest. Once at the site, the user can purchase products or services or click-thru to, or be redirected to, online merchants that provide the products and services of interest. The infomediary will typically charge a merchant a fee or commission, or receive some other economic benefit from the merchant, in exchange for the service of placing a potential buyer in contact with the merchant.
With the exception of Google.com, virtually all search engines on the Internet today (circa 2002) seek to maximize their revenues by charging for search results placement. The concept of Pay-Per-Click (“PPC”) was pioneered by GoTo.com (now Overture) and, while initially derided, has become the predominant method by which buyers meet sellers on the Internet. In a few short years, GoTo's introduction of the auction principle to online marketing transformed old-fashioned media buys into a real-time market place not unlike a stock exchange.
From the highly dynamic and multi-dimensional nature of such marketplaces arises a critical problem that plagues most of today's eCommerce sites: what is the optimal way to acquire PPC traffic? The present invention solves that problem.
On the surface, the apparently simple question of how much an eCommerce site should bid on a keyword begs an equally simple answer: less than it's worth. In other words, if the profit from an average visitor to a site is a dollar, the bid policy would be to spend as little as necessary—certainly less than a dollar—to get that visitor in the door.
On the surface, this approach—let's call it a bidding strategy—appears plausible. In reality, though, the most profitable expenditure of a given marketing budget is complicated by the following factors:                the number of keywords to bid on may be in the tens of thousands, with each keyword vying for a share of the budget        there are numerous search engines available for keyword placement, each with unique financial characteristics        the amount of traffic received from a keyword depends on the rank in the search results (which depends on the amount bid . . . )        rank is, of course, also determined by competitors' bids (which may be influenced by our bid . . . )        bids on a keyword are updated at increasing frequency, often several times per hour, if not minute        the revenue per click is uncertain and varies from keyword to keyword        the value of a new customer exceeds that of the expected profit per visit as a function of, among other factors, the expected lifetime value (LTV) of a new customer        temporal, seasonal, geographical and other factors influence the RPC of a keyword.        
These insights give rise to increasingly sophisticated bid strategies such as                computing the expected revenue for each keyword and bidding some fraction thereof        eliminating any gap between one's own bid and that of the next-lowest competitor        adjusting bids for relative ‘quality of traffic’, i.e. aggregate conversion, received from different search engines        multiplying the expected profit per click with the expected traffic for each search results position and maximizing that product, etc.        
Not only are none of the above strategies or combinations thereof optimal, they are not even ‘satisficing’. Only the present invention computes a solution to the bidding conundrum that is the best possible one given available computing resources.
The Internet and the systems operated thereon have proven to be great facilitators of commerce. Yet these systems are still young and inefficient. In particular there is a need for improved systems that enable buyers and sellers to easily and efficiently reach each other regarding products and services offered over the Internet. As the real-life use of the present invention at assignee BizRate.com has demonstrated, its impact on an eBusiness' bottomline may well be significantly improved, enabling the infomediary to provide consumers with superior shopping opportunities.